Big Data, Big Dupe: A little book about a big bunch of nonsense

by Stephen Few

Big Data, Big Dupe is a little book about a big bunch of nonsense. The story of David and Goliath inspires us to hope that something little, when armed with truth, can topple something big that is a lie. This is the author's hope. While others have written about the dangers of Big Data, Stephen Few reveals the deceit that belies its illusory nature. If "data is the new oil," Big Data is the new snake oil. It isn't real. It's a marketing campaign that has distracted us for years from the real and important work of deriving value from data.
Big Data, Big Dupe gives a voice to the small army of data professionals who work silently and unheralded in the trenches to make sense of data. Data professionals (data analysts, statisticians, etc.) struggle to maintain focus amidst the constant distraction of Big Data nonsense. They recognize Big Data for what it a meaningless term, but also a well-funded marketing campaign that sends organizations on costly and wasteful pursuits. As IT vendors, consultants, and many academics sing the praises of Big Data, the real work of data sensemaking is being done by seasoned professionals using skills that they developed through years of study and practice. These skills existed long before the nonsense of Big Data arose. 

If you're one of these seasoned data professionals, buy this book, confirm that it speaks your truth, and then place copies on the desks of those whose foolish IT strategy and purchasing decisions are wasting your time and subverting your efforts. Data holds great promise, but that promise will forever remain unfulfilled by those who pursue the Big Data illusion rather than investing in the time-proven skills and hard work of data sensemaking.

Genres: Business, Nonfiction

96 pages, Paperback

Published February 1, 2018

Reviews

[Jesse Ofner · Rating 4 out of 5]

Mr. Few's short but poignant slaughter of Big Data as a panacea is on point. Organizations claim they need analytics and more data and better data, yet many of them lack interesting questions and/or the people who have the skills to make sense of the data. I have read and shared many the article about the promise of "Big Data" however, I've also maintained that the term itself means nothing. Size is relative and scaling matters to the processing power needed, but it doesn't as Mr Few correctly asserts alleviate the ills that plague data review and analysis since the days that it was first collected in the form of clay tablets or knotted ropes. It doesn't dissolve axioms like, "Good Data In, Good Data Out" and it does not mean that models became immutable laws of the universe. Models are (by definition) not reality, they are a means of inquiry and measurement. Reality can deviate widely from normal in the sense of standard deviations and those outliers can change the course of history. It's a good reminder that the only way we advance our knowledge as a collective society is to be rigorous in our skepticism as we search for shared truths.

[Marcelo · Rating 4 out of 5]

It's a concise and precise book, raising lots of questions about the hype around the cloudy (pun intended, please) notion of "big data". I particularly like the digs Mr. Few reserves to the book put out some five years ago by Cukier/Meyer-Schönberg.

The criticism Mr. Few does to the hype has nothing really new for those of us who are used to making sense out of data and to being wary of digital snake oil salesmen. But the elegance he does it with is for... Few.

[Julie · Rating 3 out of 5]

My point has been that I don’t care about big data for the reasons in chapters 4-6. I’ve had this fight with people who think they’re a lot smarter than they are hundreds of times. I enjoyed the beginning of the book where he establishes that big data isn’t a real thing. Anyway, this was a quick read and I liked seeing this spelled out.

[Omisile Kehinde Olugbenga · Rating 4 out of 5]

I really like the argument, helped me gain better perspective and focus on what is really important. Working in analytics can be tough if we have to deal with cliches that add no significant value to the work you do but create insatiable appetite for food you've already eaten.

[Matthew Coleman · Rating 3 out of 5]

A quick read but worthwhile to correct some of the gullible assumptions one might make around the Big Data buzzword hype.

Stephen Few calls out those who are unknowingly or otherwise, promoting the hype to the benefit of software vendors, source providers and consultancies.

He makes an important point to call out those who are threatening the merits and use of the scientific method. For example non-subject matter experts using correlations between large datasets to confirm hypotheses before any further validation and without proving causation.

The final chapter entitled Big data, Big brother is most interesting for me. We must be wary as a society not to allow institutions over exploit quantitative measures about us which do not fairly or accurately reflect the reality. For example where judgement of character must be made, computers do an awful job of fair assessment and should never be conclusive entirely on their own for making any decision with material impact on a humans life or ability to make a living. Loan/mortgage approvals for example.

[Mary Pat · Rating 4 out of 5]

Very short, and a good chunk of it is quoting other people's stuff -- that said, the message is important.

The most important part is the list of 9 items on the last page of the text - what organizations should be doing re: data instead of getting caught up in the Big Data hype.

This is one of those books where the most value would come from reading it backwards - quibbling over the definition of Big Data (which is obviously just a marketing/buzzterm) is the least important (chapter 1), and what people should actually be doing is most important (the epilogue). Try doing the reading-backward thing sometime... really can improve one's experience of business-related books.

[Wendy · Rating 3 out of 5]

While I found Stephen's earlier books, especially "Show Me the Numbers" excellent, life-altering reads, this one was longer than it needed to be to make the point. I agreed with his premise that "big data" is often used as just a splashy marketing hook instead of a term with any real meaning -- and I did so pretty quickly. The opening arguments were convincing enough that I have to confess my reading pace slowed down considerably after the first few chapters. I'll definitely keep it as a handy reference, but probably won't finish reading the entire book in-depth.

[Avi-Gil · Rating 4 out of 5]

A short (80 page) takedown of the Big Data marketing machine. Few argues that Big Data is really just data - nothing different today than before we started calling it Big Data.

I think he is mostly right, but it reads like he is an old, grouchy, self-righteous know-it-all who is angry at the world (as did his recently retired newsletters).

I was a little surprised at the chapter about privacy, where he seems to suddenly accept Big Data and then talk about the privacy issues that surround it. This didn't seem to fit with the rest of the book.

[Daniel · Rating 3 out of 5]

I am not that familiar with big data or data. So I thought why not read Big data big dupe. I liked that the book was a quick read. Even I could read it in no time at all. Its so tough for me to think about what I want to write for reviews of books. I try my best. So what else can I say about big data big dupe?
I learned that there can be a lot of data but, maybe not a lot of it can be useful. And I also learned that getting useful information from data takes thought and trained people. I think it was worth the time I spent reading it.

[Steven De Landtsheer · Rating 4 out of 5]

Stephen Few knows a lot about data and especially visualisation. So this book is totaly different. He is on a mission to convince us that Big Data is mostly a marketing gimmick and that it is in essence not different from the data we have been processing since the first computer (who was human by the way). In my case, Stephen is preaching to a converted, but if you are serious about a career in data, I recommend you to read this book and be challenged by the provocative points Stephen raises.

[Manuel Frias · Rating 4 out of 5]

This book is very short. So short that, in fact, some people tried to convince Few to make it longer. He refused and I think he did a good choice. He needed less than 100 pages to convince me that Big Data is just a nonsense marketing campaign. Extraordinarily successful though.

[Maurizio Codogno · Rating 4 out of 5]

Non farsi ammaliare dai Big Data

Come contraltare a Caos quotidiano che aveva tessuto le lodi della non-strtutturazione ho preso questo libretto che ha una tesi completamente diversa: i Big Data non sono altro che l'abbindolamento che ci fa chi vende hardware e servizi di rete. Per amor di completezza, Few con i dati ci lavora; la sua tesi però - esposta in capitoli dai titoli esplicativi "Big Data, Big Whoop", "Big Data, Big Confusion", "Big Data, Big Illusion", "Big Data, Big Ruse", "Big Data, Big Distraction", "Big Data, Big Regression", è che in realtà non c'è nulla di davvero nuovo, nemmeno la grandezza relativa dei dati in questione; quello di cui abbiamo bisogno è avere persone in grado di comprendere i dati, e non credere che le macchine possano fare tutto da sole. Quello che funziona in realtà non sono i Big Data, ma per esempio il machine learning. Generalmente io sono d'accordo con Fry, anche se non arrivo alle sue posizioni talebane di un movimento Slow Data. D'altra parte, il penultimo capitolo "Big Data, Big Brother" dimostra che questi dati vengono usati eccome...